Phytoplankton biomass, indicated by chlorophyll-a (Chl-a) concentration, is fundamentally important for aquatic ecosystems. However, accurately simulating Chl-a is always challenging even when using state-of-the-art numerical models. We propose a data-driven modeling framework that combines Empirical Orthogonal Function (EOF) analysis and machine-learning technique to tackle this problem, using Chesapeake Bay as an example. Through the dimension reduction using EOF, the three-dimensional (3D) problem can be decomposed into multiple one-dimensional (1D) problems. The non-linearity of these 1D problems will be modeled with machine learning using an artificial neural network. Model performance in terms of spatiotemporal Chl-a variations with both seasonal and interannual signals is evaluated. The model performance is comparable or higher than 3D numerical models previously applied in Chesapeake Bay. Sensitivity tests reveal the necessity of forcing transformations to improve the model predictive skill. Instead of manually applying a transformation for each input forcing variable, an auto-selection procedure is adopted to choose an appropriate transformation from a variety of transformation options. While it is unlikely the data-approach can replace the traditional numerical models, we argue that data-driven approaches provide a promising way for future studies in coastal and estuarine systems considering the fast accumulation of observational data.

叶绿素-a（Chl-a）浓度指示的浮游植物生物量对水生生态系统至关重要。但是，即使使用最新的数值模型，准确地模拟Chl-a始终具有挑战性。我们以切萨皮克湾为例，提出了一个数据驱动的建模框架，该框架结合了经验正交函数（EOF）分析和机器学习技术来解决此问题。通过使用EOF进行降维，可以将三维（3D）问题分解为多个一维（1D）问题。这些一维问题的非线性将使用人工神经网络通过机器学习进行建模。评估了时空Chl-a随季节和年际信号变化的模型性能。该模型的性能与之前在切萨皮克湾应用的3D数值模型相当或更高。敏感性测试表明，必须进行强制转换以提高模型预测能力。代替为每个输入强制变量手动应用转换，而是采用自动选择过程从多种转换选项中选择适当的转换。尽管数据方法不可能取代传统的数值模型，但我们认为，考虑到快速积累的观测数据，数据驱动的方法为沿海和河口系统的未来研究提供了一种有希望的方法。代替为每个输入强制变量手动应用转换，而是采用自动选择过程从多种转换选项中选择适当的转换。尽管数据方法不可能取代传统的数值模型，但我们认为，考虑到快速积累的观测数据，数据驱动的方法为沿海和河口系统的未来研究提供了一种有希望的方法。代替为每个输入强制变量手动应用转换，而是采用自动选择过程从多种转换选项中选择适当的转换。尽管数据方法不可能取代传统的数值模型，但我们认为，考虑到快速积累的观测数据，数据驱动的方法为沿海和河口系统的未来研究提供了一种有希望的方法。